Digital signal processing for the detection of hidden objects using an FMCW radar.

Abstract

This thesis deals with the detection of hidden objects using a short-range frequency-modulated continuous wave (FMCW) radar. The detection is carried out by examining the estimated Power Spectral Density (PSD) functions of sampled returns, the peaks of which theoretically correspond to the reflecting surfaces of hidden objects. Fourier and non-Fourier PSD estimation algorithms are applied to the radar returns to extract information on the hidden surfaces. The Fourier methods used are Direct, Blackman-Tukey, Bartlett, and Smoothed Periodograms. The different PSDs are compared, and the validity of each PSD is then discussed. The study is new for this type of radar and the results are used as references for other PSD estimations. Non-Fourier methods offer many advantages. Firstly the Autoregressive Process (AR) is used for this particular application. As well as PSDs the noise spectra are also produced to show the performance of the chosen models. An alternative approach to the conventional forward-backward residuals ( e. g. Burg's method) or autocorrelation and covariance methods ( as those used in speech analysis ) is introduced in this thesis. The stability and good resolution of the PSDs is obtained by a better estimation of the autocovariance coefficients (ACF) from the data available : averaging two p-shifted ACF calculated by covariance method. Once the covariance coefficients are found, the Levinson-Durbin recursive algorithm is used to get the model parameters and the PSDs. Two other non-conventional methods are also attempted to show the image of hidden objects. They are Pisarenko Harmonic Decomposition method and Prony energy spectrum density estimation. In addition to the one-dimensional processing stated above, this thesis extends it to two-dimensional cases, which give more information on the shape of hidden objects.